@inproceedings{bagheri-garakani-etal-2022-improving,
title = "Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity",
author = "Bagheri Garakani, Alireza and
Yang, Fan and
Hua, Wen-Yu and
Chen, Yetian and
Momma, Michinari and
Deng, Jingyuan and
Gao, Yan and
Sun, Yi",
editor = "Malmasi, Shervin and
Rokhlenko, Oleg and
Ueffing, Nicola and
Guy, Ido and
Agichtein, Eugene and
Kallumadi, Surya",
booktitle = "Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ecnlp-1.6/",
doi = "10.18653/v1/2022.ecnlp-1.6",
pages = "44--48",
abstract = "Ensuring relevance quality in product search is a critical task as it impacts the customer`s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term. In this work we leverage a high-precision cross-encoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization. We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking."
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<abstract>Ensuring relevance quality in product search is a critical task as it impacts the customer‘s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term. In this work we leverage a high-precision cross-encoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization. We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking.</abstract>
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%0 Conference Proceedings
%T Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity
%A Bagheri Garakani, Alireza
%A Yang, Fan
%A Hua, Wen-Yu
%A Chen, Yetian
%A Momma, Michinari
%A Deng, Jingyuan
%A Gao, Yan
%A Sun, Yi
%Y Malmasi, Shervin
%Y Rokhlenko, Oleg
%Y Ueffing, Nicola
%Y Guy, Ido
%Y Agichtein, Eugene
%Y Kallumadi, Surya
%S Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F bagheri-garakani-etal-2022-improving
%X Ensuring relevance quality in product search is a critical task as it impacts the customer‘s ability to find intended products in the short-term as well as the general perception and trust of the e-commerce system in the long term. In this work we leverage a high-precision cross-encoder BERT model for semantic similarity between customer query and products and survey its effectiveness for three ranking applications where offline-generated scores could be used: (1) as an offline metric for estimating relevance quality impact, (2) as a re-ranking feature covering head/torso queries, and (3) as a training objective for optimization. We present results on effectiveness of this strategy for the large e-commerce setting, which has general applicability for choice of other high-precision models and tasks in ranking.
%R 10.18653/v1/2022.ecnlp-1.6
%U https://aclanthology.org/2022.ecnlp-1.6/
%U https://doi.org/10.18653/v1/2022.ecnlp-1.6
%P 44-48
Markdown (Informal)
[Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity](https://aclanthology.org/2022.ecnlp-1.6/) (Bagheri Garakani et al., ECNLP 2022)
ACL
- Alireza Bagheri Garakani, Fan Yang, Wen-Yu Hua, Yetian Chen, Michinari Momma, Jingyuan Deng, Yan Gao, and Yi Sun. 2022. Improving Relevance Quality in Product Search using High-Precision Query-Product Semantic Similarity. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 44–48, Dublin, Ireland. Association for Computational Linguistics.